In addition, we select some state-of-the-art pose
transfer methods as baselines to compare with our method , including PG2 [1]}, DefGAN [2]}, PATN [3]} and ADGAN [4]}. Among them, Def-GAN and PATN model the deformation between the source image and the target image, but PG2 and ADGAN do not consider it. The result of the comparison method is provided by authors or obtained by open source code and pre-trained model. The qualitative comparison result is shown in Figure REF . We can learn from the results, on the one hand, compared with PG2 and ADGAN, which generate the a blurry and coarse results without considering the establishment of the dense correspondence between the source image and the target image, while our method has the correct pose and more detailed texture results due to model the deformation. On the other hand, compared with other methods that model deformation, such as DefGAN and PATN, our method can also generate more natural and realistic results due to the mutual promotion of dense correspondence and decomposed attribute modules, especially on faces identity and hair texture.
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